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Spatial Regression Models: A Systematic Comparison of Different Model Specifications using Monte Carlo Experiments

  • Spatial regression models provide the opportunity to analyse spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarises the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a non-spatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive (SAR) and spatial error (SEM) specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) as well as the simple SLX provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect `spillover' effects, several - quite realistic - situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.
Metadaten
Author:Tobias RüttenauerORCiD
URN (permanent link):urn:nbn:de:hbz:386-kluedo-56301
Parent Title (English):Sociological Methods and Research
Document Type:Preprint
Language of publication:English
Publication Date:2019/06/09
Year of Publication:2019
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2019/06/11
Number of page:37
Source:https://journals.sagepub.com/home/smr
Faculties / Organisational entities:Fachbereich Sozialwissenschaften
DDC-Cassification:3 Sozialwissenschaften / 300 Sozialwissenschaften, Soziologie, Anthropologie
Licence (German):Zweitveröffentlichung